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Setup and Installation

To use this feature, install the langchain-hana package: 
pip install langchain_hana
And then, create a connection to your SAP HANA Cloud instance. 
import os

from dotenv import load_dotenv
from hdbcli import dbapi

# Load environment variables if needed
load_dotenv()

# Establish connection to SAP HANA Cloud
connection = dbapi.connect(
    address=os.environ.get("HANA_DB_ADDRESS"),
    port=os.environ.get("HANA_DB_PORT"),
    user=os.environ.get("HANA_DB_USER"),
    password=os.environ.get("HANA_DB_PASSWORD")
)
HanaSparqlQAChain ties together:
  1. Schema-aware SPARQL generation
  2. Query execution against SAP HANA
  3. Natural-language answer formatting

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Initialization

You need:
  • An LLM to generate and interpret queries
  • A HanaRdfGraph (with connection, graph_uri, and ontology)
Follow the steps here HanaRdfGraph to know more about creating a HanaRdfGraph instance. Import the HanaSparqlQAChain 
from langchain_hana import HanaSparqlQAChain

qa_chain = HanaSparqlQAChain.from_llm(
    llm=llm, graph=graph, allow_dangerous_requests=True, verbose=True
)

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Pipeline overview

  1. SPARQL Generation
  • Uses SPARQL_GENERATION_SELECT_PROMPT
  • Inputs:
    • schema (Turtle from graph.get_schema)
    • prompt (user’s question)
  1. Query Post-processing
  • Extracts the SPARQL code from the llm output.
  • Inject FROM <graph_uri> if missing
  • Ensure required common prefixes are declared (rdf:, rdfs:, owl:, xsd:)
  1. Execution
  • Calls graph.query(generated_sparql)
  1. Answer Formulation
  • Uses SPARQL_QA_PROMPT
  • Inputs:
    • context (raw query results)
    • prompt (original question)

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Prompt templates

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”SPARQL Generation” prompt

The sparql_generation_prompt is used to guide the LLM in generating a SPARQL query from the user question and the provided schema.

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Answering prompt

The qa_prompt instructs the LLM to create a natural language answer based solely on the database results. The default prompts can be found here: prompts.py

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Customizing prompts

You can override the defaults at initialization: 
qa_chain = HanaSparqlQAChain.from_llm(
    llm=llm,
    graph=graph,
    allow_dangerous_requests=True,
    verbose=True,
    sparql_generation_prompt=YOUR_SPARQL_PROMPT,
    qa_prompt=YOUR_QA_PROMPT
)
  • sparql_generation_prompt must have the input variables: ["schema", "prompt"]
  • qa_prompt must have the input variables: ["context", "prompt"]

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Example: Question answering over a “Movies” knowledge graph

Prerequisite: You must have an SAP HANA Cloud instance with the triple store feature enabled. For detailed instructions, refer to: Enable Triple Store
Load the kgdocu_movies example data. See Knowledge Graph Example. Below we’ll:
  1. Instantiate the HanaRdfGraph pointing at our “movies” data graph
  2. Wrap it in a HanaSparqlQAChain powered by an LLM
  3. Ask natural-language questions and print out the chain’s responses
This demonstrates how the LLM generates SPARQL under the hood, executes it against SAP HANA, and returns a human-readable answer. First, create a connection to your SAP HANA Cloud instance. 
import os

from dotenv import load_dotenv
from hdbcli import dbapi

# Load environment variables if needed
load_dotenv()

# Establish connection to SAP HANA Cloud
connection = dbapi.connect(
    address=os.environ.get("HANA_DB_ADDRESS"),
    port=os.environ.get("HANA_DB_PORT"),
    user=os.environ.get("HANA_DB_USER"),
    password=os.environ.get("HANA_DB_PASSWORD")
)
Then, set up the knowledge graph instance 
from gen_ai_hub.proxy.langchain.openai import ChatOpenAI
from langchain_hana import HanaRdfGraph, HanaSparqlQAChain

# from langchain_openai import ChatOpenAI  # or your chosen LLM

# Set up the Knowledge Graph
graph_uri = "kgdocu_movies"

graph = HanaRdfGraph(
    connection=connection,
    graph_uri=graph_uri,
    auto_extract_ontology=True
)

# a basic graph schema is extracted from the data graph. This schema will guide the LLM to generate a proper SPARQL query.
schema_graph = graph.get_schema
print(schema_graph.serialize(format="turtle"))

@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

<http://kg.demo.sap.com/acted_in> a owl:ObjectProperty ;
    rdfs:label "acted_in" ;
    rdfs:domain <http://kg.demo.sap.com/Actor> ;
    rdfs:range <http://kg.demo.sap.com/Film> .

<http://kg.demo.sap.com/dateOfBirth> a owl:DatatypeProperty ;
    rdfs:label "dateOfBirth" ;
    rdfs:domain <http://kg.demo.sap.com/Actor> ;
    rdfs:range xsd:dateTime .

<http://kg.demo.sap.com/directed> a owl:ObjectProperty ;
    rdfs:label "directed" ;
    rdfs:domain <http://kg.demo.sap.com/Director> ;
    rdfs:range <http://kg.demo.sap.com/Film> .

<http://kg.demo.sap.com/genre> a owl:ObjectProperty ;
    rdfs:label "genre" ;
    rdfs:domain <http://kg.demo.sap.com/Film> ;
    rdfs:range <http://kg.demo.sap.com/Genre> .

<http://kg.demo.sap.com/placeOfBirth> a owl:ObjectProperty ;
    rdfs:label "placeOfBirth" ;
    rdfs:domain <http://kg.demo.sap.com/Actor> ;
    rdfs:range <http://kg.demo.sap.com/Place> .

<http://kg.demo.sap.com/title> a owl:DatatypeProperty ;
    rdfs:label "title" ;
    rdfs:domain <http://kg.demo.sap.com/Film> ;
    rdfs:range xsd:string .

rdfs:label a owl:DatatypeProperty ;
    rdfs:label "label" ;
    rdfs:domain <http://kg.demo.sap.com/Actor>,
        <http://kg.demo.sap.com/Director>,
        <http://kg.demo.sap.com/Genre>,
        <http://kg.demo.sap.com/Place> ;
    rdfs:range xsd:string .

<http://kg.demo.sap.com/Director> a owl:Class ;
    rdfs:label "Director" .

<http://kg.demo.sap.com/Genre> a owl:Class ;
    rdfs:label "Genre" .

<http://kg.demo.sap.com/Place> a owl:Class ;
    rdfs:label "Place" .

<http://kg.demo.sap.com/Actor> a owl:Class ;
    rdfs:label "Actor" .

<http://kg.demo.sap.com/Film> a owl:Class ;
    rdfs:label "Film" .
After that, initialise the LLM. 
# Initialize the LLM
llm = ChatOpenAI(proxy_model_name="gpt-4o", temperature=0)
Then, we create a SPARQL QA Chain 
# Create a SPARQL QA Chain
chain = HanaSparqlQAChain.from_llm(
    llm=llm,
    verbose=True,
    allow_dangerous_requests=True,
    graph=graph,
)

# output = chain.invoke("Which movies are in the data?")
# output = chain.invoke("In which movies did Keanu Reeves and Carrie-Anne Moss play in together")
# output = chain.invoke("which movie genres are in the data?")
# output = chain.invoke("which are the two most assigned movie genres?")
# output = chain.invoke("where were the actors of "Blade Runner" born?")
# output = chain.invoke("which actors acted together in a movie and were born in the same city?")
output = chain.invoke("which actors acted in Blade Runner?")

print(output["result"])



> Entering new HanaSparqlQAChain chain...
Generated SPARQL:
\`\`\`
PREFIX kg: <http://kg.demo.sap.com/>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?actor ?actorLabel
WHERE {
    ?movie rdf:type kg:Film .
    ?movie kg:title ?movieTitle .
    ?actor kg:acted_in ?movie .
    ?actor rdfs:label ?actorLabel .
    FILTER(?movieTitle = "Blade Runner")
}
\`\`\`
Final SPARQL:

PREFIX kg: <http://kg.demo.sap.com/>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?actor ?actorLabel

FROM <kgdocu_movies>
WHERE {
    ?movie rdf:type kg:Film .
    ?movie kg:title ?movieTitle .
    ?actor kg:acted_in ?movie .
    ?actor rdfs:label ?actorLabel .
    FILTER(?movieTitle = "Blade Runner")
}

Full Context:
actor,actorLabel
http://www.wikidata.org/entity/Q1353691,Morgan Paull
http://www.wikidata.org/entity/Q1372770,William Sanderson
http://www.wikidata.org/entity/Q358990,James Hong
http://www.wikidata.org/entity/Q498420,M. Emmet Walsh
http://www.wikidata.org/entity/Q81328,Q81328
http://www.wikidata.org/entity/Q723780,Brion James
http://www.wikidata.org/entity/Q207596,Daryl Hannah
http://www.wikidata.org/entity/Q1691628,Joe Turkel
http://www.wikidata.org/entity/Q236702,Joanna Cassidy
http://www.wikidata.org/entity/Q213574,Rutger Hauer
http://www.wikidata.org/entity/Q3143555,Hy Pyke
http://www.wikidata.org/entity/Q211415,Edward James Olmos
http://www.wikidata.org/entity/Q230736,Sean Young


> Finished chain.
The actors who acted in Blade Runner are Morgan Paull, William Sanderson, James Hong, M. Emmet Walsh, Brion James, Daryl Hannah, Joe Turkel, Joanna Cassidy, Rutger Hauer, Hy Pyke, Edward James Olmos, and Sean Young.

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What’s happening under the hood?

  1. SPARQL Generation The chain invokes the LLM with your Turtle-formatted ontology (graph.get_schema) and the user’s question using the SPARQL_GENERATION_SELECT_PROMPT. The LLM then emits a valid SELECT query tailored to your schema.
  2. Pre-processing & Execution
  • Extract & clean: Pull the raw SPARQL text out of the LLM’s response.
  • Inject graph context: Add FROM <graph_uri> if it’s missing and ensure common prefixes (rdf:, rdfs:, owl:, xsd:) are declared.
  • Run on HANA: Execute the finalized query via HanaRdfGraph.query() over your named graph.
  1. Answer Formulation The returned CSV (or Turtle) results feed into the LLM again—this time with the SPARQL_QA_PROMPT. The LLM produces a concise, human-readable answer strictly based on the retrieved data, without hallucination.
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